Bottom Line:
TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference.A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels.We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

ABSTRACTThe formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

pone.0158185.g001: Illustration of a potential problem in correlation-based statistics.(A) Hypothetical time courses of two pairs of voxels (i, j) and (m, n) in three experimental trials of the same condition are shown here. It is clearly visible that the voxel pair (i, j) has a low inter-trial consistency, while the pair (m, n) has a high one. (B) In standard correlation-based statistics, the correlation between pairs of voxels is computed for each trial. Here, the correlations between (i, j) are consistent across trials, but their temporal profiles are not. In correlation-based statistics (CBS), only the consistency of correlation values is considered while the consistency of the temporal profiles within a trial is ignored. Therefore, in CBS one might erroneously conclude that voxels i, j belong to the same task-related network. The voxel pair (m, n) on the other hand also shows consistent temporal profiles and is therefore more likely to belong to the same network. (C) We propose a new measure of synchronization based on effect sizes, taking into account the inter-trial consistency. Our measure is able to separate between the voxel pairs (i, j) and (m, n); the voxel pair with low inter-trial consistency receives low scores.

Mentions:
The weak point of seed-based methods is their inability to reveal global changes of functional reorganisation. Only differences relative to the seed area can be depicted so that just a small part of the picture is revealed. Thus, a full exploration would require a multitude of seed-based analyses (i.e. one for each grey matter location) and to combine the resulting maps in a second step. It is easy to see that such a procedure constitutes a daunting multiple comparisons problem, which ultimately renders a whole-brain approach infeasible. The choice of the seed region itself may also be problematic especially if solely derived from GLM-based activation maps [22]. A further problem arises from analysing correlations in time series, as differences in correlations are in general not very reliable indicators of membership in a network. Indeed, two voxels may show strong correlations over many trials of the same experimental condition, and yet their time courses may differ widely from trial to trial, so that task-related network membership cannot be deduced from correlations alone, see Fig 1.

pone.0158185.g001: Illustration of a potential problem in correlation-based statistics.(A) Hypothetical time courses of two pairs of voxels (i, j) and (m, n) in three experimental trials of the same condition are shown here. It is clearly visible that the voxel pair (i, j) has a low inter-trial consistency, while the pair (m, n) has a high one. (B) In standard correlation-based statistics, the correlation between pairs of voxels is computed for each trial. Here, the correlations between (i, j) are consistent across trials, but their temporal profiles are not. In correlation-based statistics (CBS), only the consistency of correlation values is considered while the consistency of the temporal profiles within a trial is ignored. Therefore, in CBS one might erroneously conclude that voxels i, j belong to the same task-related network. The voxel pair (m, n) on the other hand also shows consistent temporal profiles and is therefore more likely to belong to the same network. (C) We propose a new measure of synchronization based on effect sizes, taking into account the inter-trial consistency. Our measure is able to separate between the voxel pairs (i, j) and (m, n); the voxel pair with low inter-trial consistency receives low scores.

Mentions:
The weak point of seed-based methods is their inability to reveal global changes of functional reorganisation. Only differences relative to the seed area can be depicted so that just a small part of the picture is revealed. Thus, a full exploration would require a multitude of seed-based analyses (i.e. one for each grey matter location) and to combine the resulting maps in a second step. It is easy to see that such a procedure constitutes a daunting multiple comparisons problem, which ultimately renders a whole-brain approach infeasible. The choice of the seed region itself may also be problematic especially if solely derived from GLM-based activation maps [22]. A further problem arises from analysing correlations in time series, as differences in correlations are in general not very reliable indicators of membership in a network. Indeed, two voxels may show strong correlations over many trials of the same experimental condition, and yet their time courses may differ widely from trial to trial, so that task-related network membership cannot be deduced from correlations alone, see Fig 1.

Bottom Line:
TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference.A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels.We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

ABSTRACTThe formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.